Neural Fields: 
Neural fields are emerging as useful representations for solving problems ranging from novel view synthesis [1]}, [2]}, [3]}, [4]}, [5]}, shape encoding and reconstruction [6]}, [7]}, dynamic reconstruction [8]}, [9]}, [10]}, appearance modeling [11]}, [12]}, [13]}, and human motion modeling [14]}, [15]}, [16]}, [17]}.
Generalization in neural fields to object categories remains difficult, but some methods have used pre-canonicalized datasets to circumvent this problem [18]}.
Neural fields are also used outside of computer vision, for instance in reconstructing proteins [19]}, physics [20]}, or audio [21]}.
Please see [22]}, [23]} for a more details.
In this paper, our focus is on canonicalizing for the 3D pose of neural fields, specifically neural radiance fields (NeRF) [1]}.
